Hi Andre,
Assuming that you want the exact point (date and value) where each crossing
occurs, you'll need to interpolate where they cross.
There are a number of different ways to do so, but assuming you're okay with
linear interpolation, and everything's sampled on the same dates, you can
simply do something like this:
import numpy as np
import matplotlib.pyplot as plt
def main():
x = np.linspace(0, 2*np.pi, 20)
y1 = np.sin(2*x)
y2 = np.cos(x)
crossings = find_crossings(x, y1, y2)
cross_x, cross_y = crossings.T
plt.plot(x, y1, 'bx-')
plt.plot(x, y2, 'gx-')
plt.plot(cross_x, cross_y, 'ro')
plt.show()
def find_crossings(x, y1, y2):
diff = np.diff(np.sign(y1 - y2))
indicies, = np.nonzero(diff)
crossings = [interpolate_crossing(i, x, y1, y2) for i in indicies]
return np.array(crossings)
def interpolate_crossing(i, x, y1, y2):
slope = ( (y1[i] - y2[i])
/ ((y2[i+1] - y2[i]) - (y1[i+1] - y1[i])))
x = x[i] + slope * (x[i+1] - x[i])
y = y1[i] + slope * (y1[i+1] - y1[i])
return x, y
main()
[image: VXsqp.png]
On Tue, Mar 1, 2011 at 10:07 AM, Andre Lopes <lopes80an...@gmail.com> wrote:
> Hi,
>
> I'm new to Numpy. I'm doing some tests with some Stock Market Quotes
>
> My struggle right now is "how to get the values of the moving averages
> crosses", I send an image in attach to illustrate what I'm trying to
> get.
>
> I'm using the this computation to get when the moving averages
> crosses, but when I look at the graph, the values doesn't seem ok.
>
> [quote]
> # Get when the ma20 cross ma50
> equal = np.round(ma20,2)==np.round(ma50,2)
> dates_cross = (dates[equal])
> prices_cross = (prices[equal])
> [/quote]
>
>
> The full code is this:
> [quote]
> # Modules
> import datetime
> import numpy as np
> import matplotlib.finance as finance
> import matplotlib.mlab as mlab
> import matplotlib.pyplot as plot
>
> # Define quote
> startdate = datetime.date(2008,10,1)
> today = enddate = datetime.date.today()
> ticker = 'uso'
>
> # Catch CSV
> fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
>
> # From CSV to REACARRAY
> r = mlab.csv2rec(fh); fh.close()
> # Order by Desc
> r.sort()
>
>
> ### Methods Begin
> def moving_average(x, n, type='simple'):
> """
> compute an n period moving average.
>
> type is 'simple' | 'exponential'
>
> """
> x = np.asarray(x)
> if type=='simple':
> weights = np.ones(n)
> else:
> weights = np.exp(np.linspace(-1., 0., n))
>
> weights /= weights.sum()
>
>
> a = np.convolve(x, weights, mode='full')[:len(x)]
> a[:n] = a[n]
> return a
> ### Methods End
>
>
> prices = r.adj_close
> dates = r.date
> ma20 = moving_average(prices, 20, type='simple')
> ma50 = moving_average(prices, 50, type='simple')
>
> # Get when the ma20 cross ma50
> equal = np.round(ma20,2)==np.round(ma50,2)
> dates_cross = (dates[equal])
> prices_cross = (prices[equal])
>
> # Ver se a ma20 > ma50
> # ma20_greater_than_ma50 = np.round(ma20,2) > np.round(ma50,2)
> # dates_ma20_greater_than_ma50 = (dates[ma20_greater_than_ma50])
> # prices_ma20_greater_than_ma50 = (prices[ma20_greater_than_ma50])
>
> print dates_cross
> print prices_cross
> #print dates_ma20_greater_than_ma50
> #print prices_ma20_greater_than_ma50
>
>
> plot.plot(prices)
> plot.plot(ma20)
> plot.plot(ma50)
> plot.show()
> [/quote]
>
> Someone can give me some clues?
>
> Best Regards,
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
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